Compared to evaluating a single response, this evaluation is arguably closer to the end-goal of chatbots.

such an evaluation is extremely challenging to do in a completely automatic way.


Using a feedforward NN and embedding features, TUPA MLP obtains higher scores than TUPA Sparse , but is outperformed by the LSTM parser on primary edges.

using better input encoding allowing virtual look-ahead and look-behind in the token representation, TUPA BiLSTM obtains substantially higher scores than TUPA MLP and all other parsers, on both primary and remote edges, both in the in-domain and out-of-domain settings.


Several transition-based AMR parsers have been proposed: CAMR assumes syntactically parsed input, processing dependency trees into AMR (Wang et al., 2015a(Wang et al., ,b, 2016Goodman et al., 2016).


In some instances (as in the figure), our system is nonetheless able to synthesize a close approximation.

in the most complex cases, the predictions deviate significantly from the correct implementation.


NMT proves to outperform conventional statistical machine translation (SMT) significantly across a variety of language pairs (Junczys-Dowmunt et al., 2016) and becomes the new de facto method in practical MT systems .

there still remains a severe challenge: it is hard to interpret the internal workings of NMT.


Defined on language structures with varying granularities, these translation rules are interpretable from a linguistic perspective.

nMT takes an end-to-end approach: all internal information is represented as real-valued vectors or * Corresponding author.


As test cases, we use POS tagging and Named Entity Recognition, both standard preprocessing steps for many NLP applications.

our approach is general and can also be applied to other classification tasks.


Manual annotations are often inconsistent and annotation errors can thus be identified by looking at the variance in the data.

to this, we focus on detecting errors in automatically labelled data.


Moreover, extraction is far from the way humans write summaries.

abstractive methods are able to generate better summaries with the use of arbitrary words and expressions, but generating abstractive summaries is much more difficult in practice.


Human languages exhibit a wide range of phenomena, within some limits.

some structures seem to occur or co-occur more frequently than others.


to many common problems in applied NLP, e.g., part-of-speech tagging, parsing and machine translation, the modeling of linguistic typology is fundamentally a "small-data" problem.


the models could infer that "东路" is also a location, from its character composition and neighboring words.


Recently end-to-end models have outperformed traditional pipeline approaches, predicting syntactic or semantic structure as intermediate steps, on NLU tasks such as sentiment analysis and semantic relatedness (Le and Mikolov, 2014;Kiros et al., 2015), question answering  and textual entailment (Rocktäschel et al., 2015).

the linguistic structure used in applications has predominantly been shallow, restricted to bilexical dependencies or trees.


The attention is computed as with hard attention, as α i j = softmax(u i j ).

instead of making a hard selection, a weighted average over the encoder vectors is computed as This vector is used instead of h a j for prediction and feeding to the next timestep.


The parser has an option not to be restricted by the ERG.

neither of these approaches have results available that can be compared directly to our setup, or generally available implementations.


(2014) proposed a twostage parser that first predicts concepts or subgraphs corresponding to sentence segments, and then parses these concepts into a graph structure.

mRS has a large proportion of abstract nodes that cannot be predicted from short segments, and interact closely with the graph structure.


The arc-eager-based models again give better performance, mainly due to improved concept prediction accuracy.

concept prediction remains the most important weakness of the model; Damonte et al.


If only some (not all) entities are labeled, it is not effective to learn a sequence labeling model.

every single labeled entity, along with its contexts, may be used to learn the proposed model.


it requires fixed-size inputs and lacks the ability of capturing long-term dependency.


In terms of statistical significance, our best score on the SemEval dataset (908 samples) is not significant at the 95% confidence level.

the accuracy improvements of PreWin over the common baselines are highly statistically significant with 99.9%+ confidence.


However, the method was a case of ignoring too many relevant words and accuracy was fluctuating in the mid-60% range, which is why we did not report the results.

it serves to further justify the choice of 5 words as the predicate window as fewer words caused the model to underperform.


Image representation learning has been successful via supervision from very large object-labeled datasets.

similar amounts of supervision are lacking for video representation learning.


Ensemble and multi-view learning were helpful for the Cookie Theft dataset, in which multi-view learning achieved the highest accuracy (65% of accuracy for narrative texts, a 3% of improvement compared to the best individual classifier).

neither multi-view or ensemble learning enhanced accuracy in the Cinderella dataset, where SVM-RBF with CNE space achieved the highest accuracy (65%).


it is considered that she is going to have a chat with the system.

if she says "Set an alarm at 8 o'clock," she is probably trying to operate her smartphone.


(I) Transforming grammatical roles into feature vectors: Grammatical roles are fed to our model as indices taken from a finite vocabulary V. In the simplest scenario, V contains {S, O, X, −}.

we will see in Section 3.1 that as we include more entity-specific features, V can contain more symbols.


In academia, work on generating human-like interaction focused so far on generating responses to tweets (Ritter et al., 2011;Hasegawa et al., 2013) or taking turns in short dialogs (Li et al., 2017).

the architectures assumed in these studies implement sequence to sequence (seq2seq) mappings, which do not take into account topics, sentiments or agendas of the intended responders.


We introduce an attention-based sequence learning model for the task and investigate the effect of encoding sentence-vs. paragraph-level information.

to all previous work, our model does not rely on hand-crafted rules or a sophisticated NLP pipeline; it is instead trainable end-to-end via sequenceto-sequence learning.


Our model successfully captures this information, while H&S only performs some syntactic transformation over the input without paraphrasing.

outputs from our system are not always "perfect", for example, in pair 6, our system generates a question about the reason why birds still grow, but the most related question would be why many species still grow.


Although existing summarization algorithms come up with a generic notion of importance, it is still far from the user-specific importance as shown in Figure 1.

humans can easily assess importance given a topic or a query.


Our JOINT model explores well in terms of prioritizing the concepts which yet lack user feedback.

it gives equal probabilities to all the unseen concepts.


(3) Adversarial Deep Averaging Network Similar to our approach, the adversarial Deep Averaging Network (ADAN) also exploits adversarial training for CLTC (Chen et al., 2016).

it does not have the parallel-corpus based knowledge distillation part (which we do).


We should also point out that in Table 2, the four baseline methods (PL-LSI, PL-KCCA, PL-OPCA and PL-MC) were evaluated under the condition of using additional 100 labeled target documents for training, according to the author's report (Xiao and Guo, 2013).

our methods (CLD-KCNN and CLDFA-KCNN) were evaluated under a tougher condition, i.e., not using any labeled data in the target domains.


The text-based features are extracted from the manual transcripts of the sessions, while the audio-based features are extracted from audio segments obtained by force-aligning each session transcript with its corresponding audio.

as future work, we are considering to automatize this process by conducting automatic speaker diarization and transcription via automatic speech recognition.


", knowing that "Maigret" can refer to a TV show can greatly help disambiguate its meaning.

knowledge bases may hurt performance if used blindly.


Many attempts have been made on connecting distributed representations of KBs with text in the context of knowledge base completion (Lao et al., 2011;Gardner et al., 2014;Toutanova et al., 2015), relation extraction Chang et al., 2014;Riedel et al., 2013), and question answering (Miller et al., 2016).

these approaches model text using shallow representations such as subject/relation/object triples or bag of words.


In teaching videos of "back propagation", the concept "gradient descent" is frequently mentioned when illustrating the optimization detail of back propagation.

however, "back propagation" is unlikely to be mentioned when teaching "gradient descent".


For example, "data set" and "training set" have learning dependencies and the latter concept is more advanced than the former one.

"test set" and "training set" have no such relation when their complexity levels are similar.


Second, T-SRI has certain effectiveness for learning prerequisite relations, with F 1 ranging from 62.1 to 65.2%.

t-SRI only considers relatively simple features, such as the sequential and co-occurrence among concepts.


Different research lines have been proposed around this topic, including hypernym-hyponym relation extraction (Ritter et al., 2009;Wei et al., 2012), entity relation extraction (Zhou et al., 2006;Fan et al., 2014;Lin et al., 2015) and open relation extraction (Fader et al., 2011).

previous works mainly focus on factual relations, the extraction of cognitive relations (e.g.


Eisenstein and Barzilay (2008) discuss this within the context of topic segmentation.

6 it is unclear if this would also would happen for POS tags; there is no syntactic analogue for the sort of lexical chains important in topic segmentation.


The training time of order-o CRFs grows exponentially (O(M o+1 )) with the number of output labels M , which is typically slow even for moderate-size training data if M is large.

the training time of order-o MEMMs is linear (O(M )) with respect to M independent of o, so it can handle larger training data with higher order of dependency.


(2015) also showed that joint learning of lemmas with other morphological attributes is mutually beneficial but obtaining the gold annotated datasets is very expensive.

our model needs only lemma annotated continuous text (not POS and other tags) to learn the word morphology.


While using the semantic embedding, only distributional word vectors are used for edit tree classification.

to test the effect of the syntactic embedding exclusively, output from the character level recurrent network is fed to the second level BGRNN.


Recent advances in neural networks provide strong representational power to language models with distributed representations and unbounded dependencies based on recurrent networks (RNNs).

most language models operate by generating words by sampling from a closed vocabulary which is composed of the most frequent words in a corpus.


This enables word types that are highly predictive in context to compete with the probability of a copy event.

since we are working with an open vocabulary, this strategy is unavailable in our model, so we use the MLP formulation.


Depending on the hardware support for these operations (repeated updates of recurrent states vs. softmaxes), our model may be faster or slower.

our model will have fewer parameters than a word-based model since most of the parameters in such models live in the word projection layers, and we use LSTMs in place of these.


These works are closely related to ours in that they use the technique of score function gradient estimators (Fu, 2006;Schulman et al., 2015) for stochastic learning.


−BLEU(ỹ), over all input and output structures: In the case of full-information learning where reference outputs are available, we could evaluate all possible outputs against the reference to obtain an exact estimation of the loss function.

this is not feasible in our setting since we only receive partial feedback for a single output structure per input.


Since we want the model to learn to rank y i overỹ j , we would have to sampleỹ i word-byword from p + θ andỹ j from p − θ .

sampling all words ofỹ j from p − θ leads to translations that are neither fluent nor source-related, so we propose to randomly choose one position of y j where the next word is sampled from p − θ and sample the remaining words from p + θ .


(2014), yielding the following control variate: Note that for both types of control variates, (7) and (8), the expectationȲ is zero, simplifying the implementation.

the optimal scalarĉ has to be estimated for every entry of the gradient separately for the score function control variate.


The NMT bandit models that optimize the EL objective yield generally a much higher improvement over the out-of-domain models than the corresponding linear models: As listed in Table 4, we find improvements of between 2.33 and 2.89 BLEU points on the NC domain, and between 4.18 and 5.18 BLEU points on the TED domain.

the linear models with sparse features and hypergraph re-decoding achieved a maximum improvement of 0.82 BLEU points on NC.


As maximizing F (θ, q) involves minimizing the KL divergence, Ganchev et al.

2010present a minorization-maximization algorithm akin to EM at sentence level: directly applying posterior regularization to neural machine translation faces a major difficulty: it is hard to specify the hyper-parameter b to effectively bound the expectation of features, which are usually real-valued in translation (Och and Ney, 2002;Koehn et al., 2003;Chiang, 2005).


Therefore, it is easy to use standard stochastic gradient descent algorithms to train our model.

a major difficulty in calculating gradients is that the algorithm needs to sum over all candidate translations in an exponential search space for KL divergence.


: risks making locally optimal decisions which are actually globally sub-optimal.

an exhaustive exploration of the output space would require scoring |v| T sequences, which is intractable for most real-world models.


With the recent success of neural models for text generation, beam search has become the de-facto choice for decoding optimal output sequences (Sutskever et al., 2014).

with neural sequence models, we cannot organize beams by their explicit coverage of the input.


By narrowing down the number of advertisements that law enforcement must sift through, we endeavor to provide a real opportunity for law enforcement to intervene in the lives of victims.

there are non-trivial challenges facing this line of research: Adversarial Environment.


Such sentences involving the attacker are often irrelevant since the annotations focus on the malware and not the attacker.

the above sentence implies that the malware is a remote administration tool and hence is a relevant sentence that implies malware capability.


By analyzing the hashes listed in each APT report, we obtain a list of signatures for the malware discussed in the report.

we are unable to obtain the signatures for several hashes due to restricted distribution of malware samples.


The current list of malware signatures from Cuckoo Sandbox 3 consists of 378 signature types.

only 68 signature types have been identified for the malware discussed in the 31 documents.


Additional information about revision purposes may elicit a stronger self-reflection response in Group A participants.

in Group B, there is a significant negative correlation between the number of Rev12 and ratings for the statement "it is convenient to view my previous revisions with the system" (ρ=-.36 and p < .05).


This suggests that the character-based interface is ineffective when participants have to reflect on many changes.

draft2 to draft3 Totals #Add #delete #Modify #Add #delete # Modify  Content  294  179  33  320  27  16  869  Claims/Ideas  25  8  4  5  0  0  42  Warrant/Reasoning/Backing  166  83  7  191  13  3  463  Rebuttal/Reservation  23  1  0  13  0  0  37  General Content  50  80  18  86  13  13  260  Evidence  30  7  4  25  1  0   when comparing the number of revisions made by Group A and Group B on Rev23 (controlling for their Rev12 numbers), we did not find a significant difference.


Such approaches are able to discover homonymous senses of words, e.g., "bank" as slope versus "bank" as organisation (Di Marco and Navigli, 2012).

as the graphs are usually composed of semantically related words obtained using distributional methods (Baroni and Lenci, 2010;Biemann and Riedl, 2013), the resulting clusters by no means can be considered synsets.


Results across various configurations and methods indicate that using the weights based on the similarity scores provided by word embeddings is the best strategy for all methods except MaxMax on the English datasets.

its performance using the ones weighting does not exceed the other methods using the sim weighting.


RuWordNet is more domainspecific in terms of vocabulary, so our input set of generic synonymy dictionaries has a limited coverage on this dataset.

recall calculated on YARN is substantially higher as this resource was manually built on the basis of synonymy dictionaries used in our experiments.


That is, information about one predicate-argument relation could help to identify another predicate-argument relation.

to model such multi-predicate interactions, the joint approach in the previous studies relies heavily on syntactic information, such as part-of-speech (POS) tags and dependency relations predicted by POS taggers and syntactic parsers.


Overall, performance of both models gradually deteriorated as the number of predicates in a sentence increased, because sentences that contain many predicates are complex and difficult to analyze.

compared to the singlesequence model, the multi-sequence model suppressed performance degradation, especially for zero arguments (Zero).


The recently released MS Marco dataset (Nguyen et al., 2016) also contains independently authored questions and documents drawn from the search results.

the questions in the dataset are derived from search logs and the answers are crowdsourced.


However, the questions in the dataset are derived from search logs and the answers are crowdsourced.

trivia enthusiasts provided both questions and answers for our dataset.


We can see that ALIGN, SPME and MPME, achieve higher performance in dealing with semantic questions, because relations among entities (e.g., country-capital relation for entity France and Paris) enhance the semantics in word embeddings through jointly training.

their performance for syntactic questions is weakened because more accurate semantics yields a bias to predict semantic relations even though given a syntactic query.


This is because SPME learns word embeddings and entity embeddings in separate semantic spaces, and fails to measure the similarity between context words and candidate entities.

mPmE computes the similarity between context words with mention sense instead of entities, thus achieves the best performance, which also demonstrates the high quality of the mention sense embeddings.


Moreover, the full distribution provides much richer information than point estimates for characterizing words, representing probability mass and uncertainty across a set of semantics.

since a Gaussian distribution can have only one mode, the learned uncertainty in this representation can be overly diffuse for words with multiple distinct meanings (polysemies), in order for the model to assign some density to any plausible semantics (Vilnis and McCallum, 2014).


When the input x t is a negation word, the sentiment distribution should be shifted/reversed accordingly.

the negation role is more complex than that by sentiment words, for example, the word "not" in "not good" and "not bad" have different roles in polarity change.


The RNN outscores Moses in terms of PINC and PINC * sigmoid(BLEU), meaning that its interpretations are more novel, in terms of ngrams.

this alone might not be a negative trait; according to human judgments Moses performs better in terms of fluency, adequacy and sentiment, and so the novelty of the RNN's interpretations does not necessarily contribute to their  Figure 1: An illustration of the application of SIGN to the tweet "How I love Mondays # sarcasm".


Existing active learning methods usually randomly select a set of unlabeled samples to annotate and then train the initial classifier on them (Settles, 2010).

these randomly selected samples may be redundant and not informative enough.


ILP relies on labeled samples to extract the relations among words and relations between words and sentiment expressions.

labeled samples in target domain are usually limited and the sentiment information in many unlabeled samples is not exploited in ILP.


The best performing results were achieved with two hidden layers (400 and 500 nodes respectively), tanh for activation function, and learning rate of 0.001 in gradient decent with early stopping.

the networks could not provide superior results than the SVM regressors.


In the context of multi-model learning, the method is referred to as early fusion.

late fusion approaches first learn a model on each feature set and then use/learn a meta model to combine their results.


An alternative metric, used in previous studies (Wang et al., 2013;Tsai and Wang, 2014;Kogan et al., 2009) is Mean Squared Error M SE = i (ŷ i − y i ) 2 /n.

especially when comparing models, applied on different test sets (e.g.


Network embedding (NE) is playing a critical role in network analysis, due to its ability to represent vertices with efficient low-dimensional embedding vectors.

existing NE models aim to learn a fixed context-free embedding for each vertex and neglect the diverse roles when interacting with other vertices.


1), a social-media user contacts with various friends sharing distinct interests, and a web page links to multiple pages for different purposes.

most existing NE methods only arrange one single embedding vector to each vertex, and give rise to the following two invertible issues: (1) These methods cannot flexibly cope with the aspect transition of a vertex when interacting with different neighbors.


In conventional NE models, each vertex is represented as a static embedding vector, denoted as context-free embedding.

cANE assigns dynamic embeddings to a vertex according to different neighbors it interacts with, named as context-aware embedding.


The contextfree embedding of u remains unchanged when interacting with different neighbors.

the context-aware embedding of u is dynamic when confronting different neighbors.


The attention weights over A in edge #1 are assigned to "reinforcement learning".

the weights in edge #2 are assigned to "machine learning'", "supervised learning algorithms" and "complex stochastic models".


Our work is similar to these methods in using a neural network model for knowledge sharing between different languages.

ours is different in the use of a neural stacking model, which respects the distributional differences between Singlish and English words.


This simply involves a change of word orders and thus requires no special treatments.

tag questions should be carefully analyzed in two scenarios.


Similarly, we trained a POS tagger using the Singlish dependency treebank alone with pretrained word embeddings on The Singapore Component of the International Corpus of English (ICE-SIN) (Nihilani, 1992;Ooi, 1997), which consists of both spoken and written texts.

due to limited amount of training data, the Input layer Feature layer w S Figure 5: Base parser tagging accuracy is not satisfactory even with a larger dropout rate to avoid over-fitting.


However, due to limited amount of training data, the Input layer Feature layer w S Figure 5: Base parser tagging accuracy is not satisfactory even with a larger dropout rate to avoid over-fitting.

the neural stacking structure on top of the English base model trained on UD-Eng achieves a POS tagging accuracy of 89.50% 16 , which corresponds to a 51.50% relative error reduction over the baseline Singlish model, as shown in Table 2.


Pre-trained word embeddings learned from unlabeled text have become a standard component of neural network architectures for NLP tasks.

in most cases, the recurrent network that operates on word-level representations to produce context sensitive representations is trained on relatively little labeled data.


(2017) are 3.97% for cross-lingual transfer from CoNLL 2002 Spanish NER and 6.28% F 1 for transfer from PTB POS tags.

they found only a 0.06% F 1 increase when using the full training data and transferring from both CoNLL 2000 chunks and PTB POS tags.


Current task-oriented dialogue systems (Young et al., 2013;Wen et al., 2017;Dhingra et al., 2017) require a pre-defined dialogue state (e.g., slots such as food type and price range for a restaurant searching task) and a fixed set of dialogue acts (e.g., request, inform).

human conversation often requires richer dialogue states and more nuanced, pragmatic dialogue acts.


In the above sentence, the arguments of the event include "He"(Role = P erson) and "hospital"(Role = P lace).

this paper does not focus on AE and only tackles the former task.


The correct type of the event triggered by "fired " in this case is End-Position.

it might be easily misidentified as Attack because "fired " is a multivocal word.


On the one hand, since joint methods simultaneously solve ED and AE, methods following this paradigm usually combine the loss functions of these two tasks and are jointly trained under the supervision of annotated triggers and arguments.

training corpus contains much more annotated arguments than triggers (about 9800 arguments and 5300 triggers in ACE 2005 dataset) because each trigger may be along with multiple event arguments.


Thus, the unbalanced data may cause joint models to favor AE task.

in implementation, joint models usually pre-predict several potential triggers and arguments first and then make global inference to select correct items.


On the one hand, strategy S1 only focuses on argument words, which provides accurate information to identify event type, thus ANN-S1 could achieve higher precision.

s2 focuses on both arguments and words around them, which provides more general but noised clues.


On the one hand, more positive training samples consequently make higher recall.

the extra event samples are automatically extracted from FN, thus false-positive samples are inevitable to be involved, which may result in hurting the precision.


In segLDAcop, topic 1, the top-ranked words are mostly relevant to the topic "date" (e.g.,   march, january, year, fall, february, week).

a similar topic learned by LDA appears to involve less such words (year, january, february), indicating a less coherent topic.


Given a question, we try to generate its correct semantic parse in a formal language that can be predefined by the choice of structured data source (e.g., SQL).

we push the burden of feature engineering to neural networks as in NP.


In the extreme case, the empirical distribution can be set directly as the cloud of points.

a vector representation reduces data significantly, and its effectiveness relies on the assumption that the discarded information is irrelevant or nonessential to later analysis.


Several techniques have been proposed that either extend word embedding models to cluster contexts and induce senses, usually referred to as unsupervised sense representations (Schütze, 1998;Reisinger and Mooney, 2010;Huang et al., 2012;Neelakantan et al., 2014;Guo et al., 2014;Tian et al., 2014;Šuster et al., 2016;Ettinger et al., 2016;Qiu et al., 2016) or exploit external sense inventories and lexical resources for generating sense representations for individual meanings of words Johansson and Pina, 2015;Jauhar et al., 2015;Iacobacci et al., 2015;Rothe and Schütze, 2015;Camacho-Collados et al., 2016b;Mancini et al., 2016;Pilehvar and Collier, 2016).

the integration of sense representations into deep learning models has not been so straightforward, and research in this field has often opted for alternative evaluation benchmarks such as WSD, or artificial tasks, such as word similarity.


The single model trained only on SQuAD is outperformed on all four of the datasets by the multitask model that uses distant supervision.

performance when training on SQuAD alone is not far behind, indicating that task transfer is occurring.


While it is essential for certain applications, such as machine translation, this characteristic also makes it slow to apply these models to scenarios that have long input text, such as document classification or automatic Q&A.

the fact that texts are usually written with redundancy inspires us to think about the possibility of reading selectively.


Finally, while training classifiers can be time consuming, when trained classifiers are deployed, feature extraction will dominate computation time over the classifier's lifetime.

the prediction step includes both feature extraction and computing inner products between features and weights.


In other words, its hidden layers are required to memorize the long-term dependencies and orders in the target language.

in our word-level decoder, the hidden state iterates only over the length of a chunk and then generates an end-of-chunk token.


The results show that even a standard word-based decoder has the ability to predict chunk boundaries if they are given in training data.

it is difficult for the word-based decoder to utilize the chunk information to improve the translation quality.


The decoders in the above studies can model the chunk structure by storing chunk pairs in a large table.

we do that by individually training a chunk generation model and a word prediction model with two RNNs.


7In training, our goal is to find a set of source-totarget model parameters that minimizes the training objective: With learned source-to-target model parameterŝ θ x→y , we use the standard decision rule as shown in Equation 1to find the translationŷ for a source sentence x.

a major difficulty faced by our approach is the intractability in calculating the gradients because of the exponential search space of target sentences.


The results on test set for Eureparl Corpus are 32.24 BLEU over Spanish-French translation and 24.91 BLEU over German-French translation, which are slightly better than the sent-beam method.

considering the traing time and the memory consumption, we think mode approximation is already a good way to approximate the target sentence space for sentence-level teaching.


The results are in line with our observation in Table 2 that sentence-level KL divergence by beam approximation is smaller than that by greedy approximation.

as the    Table 5: Comparison with previous work on Spanish-French translation in a zero-resource scenario over the WMT corpus.


(2016) consistently helps when used with our proposed tree encoders, with the bidirectional tree encoder remaining the best.

the improvements of the tree encoder models are smaller than that of the baseline system.


Previous work used name string match to propagate labels.

we apply self-training to label other mentions without links in Wikipedia articles even if they have different surface forms from the linked mentions (Section 2.4).


Researchers also observe that these regularities can transfer across languages.

previous endeavors to connect separate monolingual word embeddings typically require cross-lingual signals as supervision, either in the form of parallel corpus or seed lexicon.


seed lexicon) (Gouws and Søgaard, 2015;Wick et al., 2016;Duong et al., 2016;Shi et al., 2015;Mikolov et al., 2013a;Faruqui and Dyer, 2014;Lu et al., 2015;Ammar et al., 2016;Zhang et al., 2016aZhang et al., , 2017Smith et al., 2017).

our work completely removes the need for cross-lingual signals to connect monolingual word embeddings, trained on non-parallel text corpora.


These obtained high accuracies on well-formed text (e.g., news articles), which led to LD being considered solved (McNamee, 2005).

there has been renewed interest with the amount of user-generated content on the web.


There has also been document-level LD that assigns multiple language to each document (Prager, 1999;Lui et al., 2014).

documents were synthetically generated, restricted to inter-sentential language mixing.


Using word-level LD for English-Hindi (Gella et al., 2013),  observed that as much as 17% of Indian Facebook posts had codeswitching, and  showed that the native language is strongly preferred for expressing negative sentiment by English-Hindi bilinguals on Twitter.

without accurate multilingual word-level LD, there have been no largescale studies on the extent and distribution of code-switching across various communities.


Also note that there are many sentences available so that online training methods such as discriminative training of structured perceptrons can be used to learn structured predictors effectively in those settings.

for the cognates setting the unit at which there are structural constraints is the entire set of cognates for a language pair and there is only one such unit in existence (for a given language pair).


Previous work has yielded state of the art approaches that create a matrix of scores for all word pairs based on optimized weighted combinations of component scores computed on the basis of various helpful sources of information such as phonetic information, word context information, temporal context information, word frequency information, and word burstiness information.

when assigning a score to a word pair, the current state of the art methods do not take into account scores assigned to other word pairs.


RNN sequence-to-sequence models Bahdanau et al., 2015) are the state of the art for paradigm completion Kann and Schütze, 2016a;Cotterell et al., 2016a).

these models require a large amount of data to achieve competitive performance; this makes them unsuitable for out-of-thebox application to paradigm completion in the low-resource scenario.


The two metrics differ in that accuracy gives no partial credit and incorrect answers may be drastically different from the annotated form without incurring additional penalty.

edit distance gives partial credit for forms that are closer to the true answer.


The distance of the Sami languages from the Finnic (Estonian, Finnish) and Ugric (Hungarian) languages is much larger than the distances within Romance and within Slavic.

even for Northern Sami, the worst performing language, adding an additional language is still always beneficial compared to the monolingual baseline.


Our intuition is that ciphering will disrupt transfer of morphology.

6 the regularization effect we observed with Arabic should still be effective.


For 200 samples and ciphering, there is no clear difference in performance between Portuguese and Arabic.

for 50 samples and ciphering, Portuguese (0.09) seems to perform better than Arabic (0.02) in accuracy.


This may be explained by the soft-attention mechanism encouraging the encoder to encode positional information in the input representations, which may help it to predict better attention scores, and to avoid collisions when computing the weighted sum of representations for the context vector.

our hardattention model has other means of obtaining the position information in the decoder using the step actions, and for that reason it does not encode it as strongly in the representations of the inputs.


If the model were sensitive to reduplication, we would expect to see morphological variants of the query word among its nearest neighbors.


This fact additionally confirms the idea described in Section 2.2.2 that the independent optimization over parameters W and C may decrease the performance.

the target performance measure of embedding models is the correlation between semantic similarity and human assessment (Section 4.2).


The neighbourhood of "he" contains less semantically similar words in case of our model.

it filters out irrelevant words, such as "promptly" and "dumbledore".


Even syntactic formalisms are moving toward graphs (de Marneffe et al., 2014).

full semantic graphs can be expensive to annotate, and efforts are fragmented across competing semantic theories, leading to a limited number of annotations in any one formalism.


(Pennington et al., 2014) further utilizes matrix factorization on word affinity matrix to learn word representations.

these models merely arrange only one vector for each word, regardless of the fact that many words have multiple senses.


The SSA Model replaces the target word embedding with the aggregated sememe embeddings to encode sememe information into word representation learning.

each word in SSA model still has only one single representation in different contexts, which cannot deal with polysemy of most words.


Whereas for the mean rank, CBOW gets the worst results, which indicates the performance of CBOW is unstable.

although the accuracy of SAT is a bit lower than that of CBOW, SAT seldom gives an outrageous prediction.


Previous work has modeled the compositionality of words by creating characterlevel models of meaning, reducing problems of sparsity for rare words.

in many writing systems compositionality has an effect even on the character-level: the meaning of a character is derived by the sum of its parts.


The LOOKUP model learns embedding that captures the semantics of each character symbol without sharing information with each other.

the proposed VISUAL model directly learns embedding from visual information, which naturally shares information between visually similar characters.


Several techniques for reducing the rare words effects have been introduced in the literature, including spelling expansion (Habash, 2008), dictionary term expansion (Habash, 2008), proper name transliteration (Daumé and Jagarlamudi, 2011), treating words as a sequence of characters (Luong and Manning, 2016), subword units (Sennrich et al., 2015), and reading text as bytes (Gillick et al., 2015).

most of these techniques still have no mechanism for handling low frequency characters, which are the target of this work.


Semantic role labeling (SRL) is one of the fundamental tasks in natural language processing because of its important role in information extraction (Bastianelli et al., 2013), statistical machine translation (Aziz et al., 2016;Xiong et al., 2012), and so on.

state-of-the-art performance of Chinese SRL is still far from satisfactory.


Most word embedding models define a single vector for each word type.

a fundamen-tal flaw in this design is their inability to distinguish between different meanings and abstractions of the same word.


The context used for the attention component is simply the hidden state from the previous timestep.

since we use a bi-LSTM, the model essentially has two RNNs, and accordingly we have two context vectors, and associated attentions.


The input representations are enriched using syntactic context information, POS, WordNet and VerbNet (Kipper et al., 2008) information and the distance of the head word from the PP is explicitly encoded in composition architecture.

we do not use syntactic context, VerbNet and distance information, and do not explicitly encode POS information.


Related to the idea of concept embeddings is Rothe and Schütze (2015) who estimated Word-Net synset representations, given pre-trained typelevel word embeddings.

our work focuses on estimating token-level word embeddings as context sensitive distributions of concept em- beddings.


The proposed architecture introduces 4 additional parameter matrices that are optimised during training: and ← − W q .

the computational complexity and resource requirements of this model during sequence labeling are equal to the baseline from Section 2, since the language modeling components are ignored during testing and these additional weight matrices are not used.


(2015) took SAT geometry questions as their benchmark.

the nature of SAT geometry questions restricts the resulting formula's complexity.


3 The Proposed Method Intuitively, the dependency path based idea can be introduced into the temporal relation classification task.

around 64% E-E, E-T links in TimeBank-Dense are with the ends in two neighboring sentences, called cross-sentence links.


Over the rules used on the 1-best result, more than 30% are non-terminal rules, showing that the induced rules play an important role.


As shown by Durrett and Klein (2013), lexical features implicitly model some linguistic phenomena, which were previously modeled by heuristic features, but at a finer level of granularity.

we question whether the knowledge that is mainly captured by lexical features can be generalized to other domains.


Overfitting to the training dataset is a problem that cannot be completely avoided.

there is a notable overlap between the CoNLL training, development and test sets that encourages overfitting.


Durrett and Klein (2013) use exact surface forms as lexical features.

when word embeddings are used instead of surface forms, the use of lexical features is even more beneficial.


All corresponding ratios are lower than those of deep-coref in Table 5.

the ratios are surprisingly high for a system that does not use the training data.


Table 1 shows the mean value for each group and the t-statistic for each of the features.

to some previous work (Turner, 1999;Geurts et al., 2004;Spek et al., 2009), we find no betweengroup differences in raw item count.


rejecting 10% of candidates; and Area under a model's rejection curve (AUC) (Fig 3).

aUC is influenced by the base PCC of a model, making it difficult to compare the rejection performance.


Figure 2 reveals increasing disparity in LID accuracy for developing countries by the two baseline models.

eQUILID outperforms both systems at all levels of HDI and provides 30% more observations for countries with the lowest development levels.


Our proposed extrinsic evaluation approach for compound splitting is language-independent as we do not use any language-specific parameters.

in the present work we test it on the most prominent closed-compounding language, German (Ziering and van der Plas, 2014).


To address these issues and also to understand the range of possible interactions between humans and objects, the human-object interaction (HOI) detection task has been proposed, in which all possible interactions between a human and a given object have to be identified (Le et al., 2014;Chao et al., 2015;Lu et al., 2016).

both action classification and HOI detection do not consider the ambiguity that arises when verbs are used as labels, e.g., the verb play has multiple meanings in different contexts.


The HICO dataset also has multiple annotations per object and it incorporates the information that certain interactions such as riding a bike and holding a bike often co-occur.

it fails to include annotations to distinguish between multiple senses of a verb.


This is the first dataset that aims to annotate all visual senses of a verb.

the total number of images annotated and number of images for some senses is relatively small, which makes it difficult to use this dataset to train models.


Linguistic resources therefore have to play a key role if we are to make rapid progress in these language and vision tasks.

as we have shown in this paper, only a few of the existing datasets for action recognition and related tasks are based on linguistic resources (Chao et al., 2015;Gella et al., 2016;Yatskar et al., 2016).


Not surprisingly, many industry players are investing heavily in machine learning and AI to create new products and services (MIT Technology Review, 2016).

translating research into a successful product has its own challenges.


We consider two sentences paraphrases if they would have equivalent interpretations when represented as a structured query, i.e., "a pair of units of text deemed to be interchangeable" (Dras, 1999 We considered the above two questions as paraphrases since they are both requests for a list of classes, explicit and implicit, respectively, although the second one is a polar question and the first one is not.

: Prompt:Which is easier out of EECS 378 and EECS 280?


We further confirmed this by calculating PINC between the two paraphrases provided by each user, which produced scores similar to comparing with the prompt.

the One Paraphrase condition did have lower grammaticality, emphasizing the value of evaluating and filtering out workers who write ungrammatical paraphrases.


This fits our intuition that the prompt is a form of priming.

correctness decreases along the chain, suggesting the need to check paraphrases against the original sentence during the overall process, possibly using other workers as described in § 2.1.


The state shown is generated by the first six transitions of both systems.

the transition systems employed in state-of-the-art dependency parsers usually define very local transitions.


Parsers employing traditional transition systems would usually incorporate more features about the context in the transition decision, or employ beam search during parsing (Chen and Manning, 2014;Andor et al., 2016).

inspired by graph-based parsers, we propose arc-swift, which defines non-local transitions as shown in Figure 2.


4 A caveat is that the worst-case time complexity of arc-swift is O(n 2 ) instead of O(n), which existing transition-based parsers enjoy.

in practice the runtime is nearly 4 This is easy to show because in arc-eager, all Reduce transitions can be viewed as preparing for a later LArc or RArc transition.


This transition system also introduces spurious ambiguity where multiple transition sequences could lead to the same correct parse, which necessitates easy-first training to achieve a more noticeable improvement over arcstandard.

arc-swift can be easily implemented given the parser state alone, and does not give rise to spurious ambiguity.


For a comprehensive study of transition systems for dependency parsing, we refer the reader to (Bohnet et al., 2016), which proposed a generalized framework that could derive all of the traditional transition systems we described by configuring the size of the active token set and the maximum arc length, among other control parameters.

this framework does not cover arc-swift in its original form, as the authors limit each of their transitions to reduce at most one token from the active token set (the buffer).


1 Generative models defining joint distributions over parse trees and sentences are good theoretical models for interpreting natural language data, and appealing tools for tasks such as parsing, grammar induction and language modeling (Collins, 1999;Henderson, 2003;Titov and Henderson, 2007;Petrov and Klein, 2007;Dyer et al., 2016).

they often impose strong independence assumptions which restrict the use of arbitrary features for effective disambiguation.


Parsing In parsing, we are interested in the parse tree that maximizes the posterior p(a|x) (or the joint p(a, x)).

the decoder alone does not have a bottom-up recognition mechanism for computing the posterior.


The idea of using neural networks is the basis of the state-of-the-art attention-based approach to machine translation (Bahdanau et al., 2015;Luong et al., 2015).

that approach is not based on the principle of an explicit and separate lexicon model.


NMT models usually do not make explicit use of syntactic information about the languages at hand.

a large body of work was dedicated to syntax-based SMT (Williams et al., 2016).


It can accurately predict some compositional semantic effects and handle negation.

since it was trained on movie reviews, it is likely to be missing labelled data for some common phrases in our blogs.


We find it a welcome result that our semi-supervised methods yield patterns that correspond to the A&R classes, thus validating our suspicion that first-person sentences furnish a simplifying test ground for discovering functional patterns in the wild.

many patterns are not covered by A&R's general classes, see Table 6.


They then apply label propagation to spread polarity from sentences to events.

the triples they learn do not focus on first-person experiencers.


A fundamental advantage of neural models for NLP is their ability to learn representations from scratch.

in practice this often means ignoring existing external linguistic resources, e.g., Word-Net or domain specific ontologies such as the Unified Medical Language System (UMLS).


Searching directly in the generative models yields results that are partly surprising, as it reveals the presence of parses which the generative models prefer, but which lead to lower performance than the candidates proposed by the base model.

the results are also unsurprising in the sense that explicitly combining scores allows the reranking setup to achieve better performance than implicit combination, which uses only the scores of a single model.


to these methods, our work goes a step further, fully coupling the entire sequences of hidden states of an RNN.


Our work is similar to (Finkel et al., 2005), which augments a CRF with long-distance constraints.

our work differs in that we extend an RNN and uses Netwon-Krylov (Knoll and Keyes, 2004) instead of Gibbs Sampling.


For instance, as shown in Figure 1, the editor on idebate.org -a Wikipedia-style website for gathering pro and con arguments on controversial issues, utilizes arguments based on study, factual evidence, and expert opinion to support the anti-gun claim "legally owned guns are frequently stolen and used by criminals".

it would require substantial human effort to collect information from diverse resources to support argument construction.


(2015) investigates the detection of relevant factual evidence from Wikipedia articles.

it is unclear whether their method can perform well on documents of different genres (e.g.


Our task is related to caption generation, which has been studied extensively (e.g., Pedersoli et al., 2016;Carrara et al., 2016;Chen et al., 2016) with MSCOCO (Chen et al., 2015b) and Flickr30K (Young et al., 2014;Plummer et al., 2015).

to caption generation, our task does not require approximate metrics like BLEU.


The inter-sentence classifier exhibits the same trend: GS features do improve the performance.

adding cTAKES features degrades it slightly (-0.013).


(2017) tested both CNN and LSTM models and found CNN superior to LSTM.

this work addressed intra-sentence relations only.


From a global perspective, the work we have presented in this article shows that in accordance with a more general trend, our neural model for extracting containment relations clearly outperforms classical approaches based on feature engineering.

it also shows that incorporating classical features in such a model is a way to improve it, even if all kinds of features do not contribute equally to such improvement.


Discourse segmentation is a crucial step in building end-to-end discourse parsers.

discourse segmenters only exist for a few languages and domains.


Most recent works on RST discourse parsing focuses on the task of tree building, relying on a gold discourse segmentation (Ji and Eisenstein, 2014;Feng and Hirst, 2014;Li et al., 2014;Joty et al., 2013).

discourse parsers' performance drops by 12-14% when relying on predicted segmentation (Joty et al., 2015), underscoring the importance of discourse segmentation.


POS tagger, chunker, list of connectives, gold sentences).

we present what is to the best of our knowledge the first work on discourse segmentation that is directly applicable to low-resource languages, presenting results for scenarios where no labeled data is available for the target language.


Our scores are not directly comparable with sentence-level state-of-the-art systems (see Section 2).

for En-DT, our best system correctly identifies 950 sentence boundaries out of 991, but gets only 84.5% in F 1 for intra-sentential boundaries, 11 thus lower than the state-of-the-art (91.0%).


Also, global sufficiency has the lowest agreement in both cases.

the experts hardly said "cannot judge" at all, whereas the crowd chose it for about 4% of all ratings (most often for global sufficiency), possibly due to a lack of training.


Considering that some common reasons are quite vague, the diverse and comprehensive theoretical view of argumentation quality may guide a more insightful assessment.

some quality dimensions remain hard to assess and/or to separate in practice, resulting in limited agreement.


Similarly, when we pack w 3,9 into an oracle summary, we have to pack both chunks c 3,3 and c 3,5 and drop chunk c 3,4 .

this compres-sion is not allowed since there is no dependency relationship between c 3,3 and c 3,5 .


Firstly, to restrict the training data to grammatical and informative sentences, only news articles satisfying certain conditions are used.

then, nouns, verbs, adjectives, and adverbs (i.e., content words) shared by S and H are identified by matching word lemmas, and a rooted dependency subtree that contains all the shared content words is regarded as C. their method is designed for English, and cannot be applied to Japanese as it is.


Thus, in C, original forms are replaced by their abbreviated forms obtained as explained in Section 2.1 (e.g., the pair with "-" in Figures  1 and 2).

we do not allow the head of a chunk to be deleted to keep the grammaticality.


rooted includes only deletion of the leafs in a dependency tree.

multi-root+ includes deleting the global root and reflecting abbreviated forms besides it.


They make it easy to include synonyms and word-class information through hypernym relations.

they require substantial human effort to build and can have low coverage.


In both SF and pocket KBP, a query is an entity of interest and a document mentioning that entity.

in PKB the primary goal is to populate the KB with nodes for all entities related to the query, irrespective of any prior beliefs about relations.


Structured curated KBs have been used successfully for this task (Berant et al., 2013;Berant and Liang, 2014).

these KBs are expensive to build and typically domain-specific.


It is important to note that existing Open IE systems, like Open IE 4.2, may also extract numerical facts.

they are oblivious to the presence of numbers in arguments.


An extension to the Universal Schema approach was proposed by (Toutanova et al., 2015), where representations of text relations are formed compositionally by Convolutional Neural Networks (CNNs) and then composed with entity vectors by a bilinear model to score a fact.

these models show only moderate improvement when incorporating tex-  A limitation of the Universal Schema approach for joint embedding of KBs and text is that information about the correspondence between KB and text relations is only implicitly available through their co-occurrence with entities.


in tasks which prefer dense semantic representations (e.g.


In these experiments, there are a few sub-category labels that are not included in the training data.

we still hope that our model could still return the correct parent category for these unseen subcategories at test time.


Information extraction (IE) from text has largely focused on relations between individual entities, such as who has won which award.

some facts are never fully mentioned, and no IE method has perfect recall.


Ideally, we would like to make sense of all cardinality statements found in text.

this would require us to resolve the meaning of a large set of vague predicates, which is in general a difficult task.


This problem has matured into learning semantic parsers from parallel question and logical form pairs (Zelle and Mooney, 1996;Zettlemoyer and Collins, 2005), to recent scaling of methods to work on very large KBs like Freebase using question and answer pairs (Berant et al., 2013).

a major drawback of this paradigm is that KBs are highly incomplete (Dong et al., 2014).


Sequence-to-Sequence (seq2seq) models have demonstrated excellent performance in several tasks including machine translation (Sutskever et al., 2014), summarization (Rush et al., 2015, dialogue generation (Serban et al., 2015), and image captioning (Xu et al., 2015).

the standard cross-entropy training procedure for these models suffers from the well-known problem of exposure bias: because cross-entropy training always uses gold contexts, the states and contexts encountered during training do not match those encountered at test time.


Note that our original motivation based on removing discontinuity does not strictly apply to this sampling procedure, which still yields a stochastic gradient due to sampling from the Gumbel distribution.

this approach is conceptually related to greedy relaxations since, here, the soft argmax reparametrization reduces gradient variance which may yield a more informative training signal.


Among them, word-level combination approaches that adopt confusion network for decoding have been quite successful (Rosti et al., 2007;Ayan et al., 2008;Freitag et al., 2014).

these approaches are mainly designed for SMT without considering the features of NMT results.


As for negative examples, Munteanu and Marcu (2005) randomly paired sentences from their parallel data using two constraints: a length ratio not greater than two, and a coverage constraint that considers a negative example only if more than half of the words of the source sentence has a translation in the given target sentence according to some bilingual lexicon.

from a large parallel corpus, one can easily retrieve another target sentence, almost identical, containing most of the words that the true target sentence also contains.


Standard tasks, such as topic classification, are usually performed within a single language, and the maximum feature space size is a function of the single language's vocabulary.

lID must deal with vocabulary from many languages and the feature space grows prodigiously.


Otherwise, it is usually impossible to combine these pieces of information effectively.

the standard syntactic corpus of English, Penn Treebank, is not concerned with consistency between syntactic trees and spans of multiword expressions (MWEs).


Simple, yet competitive methods, are based on pointwise vector addition or multiplication Lapata, 2008, 2010).

these approaches neglect the structure of the text defining composition as a commutative operation.


Most of the existing models generate one representation per word and do not distinguish between different meanings of a word.

many tasks can benefit from using multiple representations per word to capture polysemy (Reisinger and Mooney, 2010).


• Fixed granularity: in some cases, annotators might feel too restricted with a given rating scale and may want to place an item inbetween the two points on the scale.

a fine-grained scale may overwhelm the respondents and lead to even more inconsistencies in annotation.


BWS is claimed to produce high-quality annotations while still keeping the number of annotations small (1.5N -2N tuples need to be annotated) (Louviere et al., 2015;Kiritchenko and Mohammad, 2016a).

the veracity of this claim has never been systematically established.


The sparse prior work in natural language annotations that uses BWS involves the creation of datasets for relational similarity (Jurgens et al., 2012), word-sense disambiguation (Jurgens, 2013), and word-sentiment intensity (Kiritchenko and Mohammad, 2016a).

none of these works has systematically compared BWS with the rating scale method.


(Estimating a value often stabilizes as the sample size is increased.)

in rating scale annotation, each item is annotated individually whereas in BWS, groups of four items (4-tuples) are annotated together (and each item is present in multiple different 4-tuples).


On the one hand, for BWS, the respondent has to consider four items at a time simultaneously.

even though a rating scale question explicitly involves only one item, the respondent must choose a score that places it appropriately with respect to other items.


So we weigh such words higher than the other words in the description during gender prediction task.

these weights might not be applicable for a location prediction task.


Hierarchical-Attention model performs well ahead of the other two models for almost all the tasks.

the performance of all the models fall flat for location prediction task.


It is quite valuable to analyze and predict user opinions from these materials (Wang and Pal, 2015), in which supervised learning is one of the effective paradigms (Xu et al., 2015).

the performance of a supervised learning algorithm relies heavily on the quality of training labels (Song et al., 2015).


At this optimal setting, the baseline is outperformed by 2.2%.

if more than only the first candidate is used, it is not beneficial to normalize all words anymore.


Deep latent variable models have been shown to facilitate the response generation for open-domain dialog systems.

these latent variables are highly randomized, leading to uncontrollable generated responses.


This approach, however, requires a set of high-quality labeled data (i.e., the Gold Standard) for providing the instruction and feedback to the crowd workers.

acquiring such data requires a considerable amount of human effort.


In both our method and GI, the crowd workers are trained before enrolling in the main task.

gI trains annotators using gold Standard data, which involves a higher level of supervision with respect to our method.


3 There have been other works with different experimental setups (Gambäck et al., 2011;Webb and Ferguson, 2010) that obtained accuracies ranging from 77.85% to 80.72%.

these results are not directly comparable to ours.


We believe that the difference in performance is not due to the attention mechanism being ineffective, but because Shen and Lee (2016) treat the classification of each utterance independently.

kalchbrenner and Blunsom (2013) take the sequential nature of dialog acts into account, and run an RNN across the conversation, which conditions the generation of a dialogue act on the dialogue acts and utterances in all the previous dialogue turns.


the top keyphrases SR selects, which are highly common words in scientific papers.

when α = 1 these keyphrases are not presented among the top.


Other methods, such as probabilistic latent semantic indexing (Hofmann, 1999), nonnegative matrix factorization (Sra and Inderjit, 2006), are viable alternatives.

it is hard to tell in general if the keyphrase quality improves with these alternatives.


EL also plays an important role in mining customer opinions from data generated on social platforms and ecommerce websites, thereby helping companies better understand the needs and expectations of their customers.

the target products are often not covered by general KBs.


Next, we proceed to score each candidate e i,j and determine which one m i should be linked to.

we have no knowledge of the target entities except for names and thus can't directly compare m i with them.


An ever increasing amount of data is becoming available for NMT training.

only the in-domain or relateddomain corpora tend to have a positive impact on NMT performance.


Each word x i is represented by concatenating the forward hidden state − → h i and the backward one In this way, the source sentence X = {x 1 , ..., x Tx } can be represented as annotations H = {h 1 , ..., h Tx }.

in the decoder, an RNN hidden state s j for time j is computed by: The context vector c j is then, computed as a weighted sum of these annotations H = {h 1 , ..., h Tx }, by using alignment weight α ji : 3 Sentence Embedding and Selection A source sentence can be represented as the annotations H. the length of H depends on the sentence length T x .


Next we observe that both back-translation and our proposed TDA method significantly improve translation quality.

tDA obtains the best results overall and significantly outperforms backtranslation in all test sets.


when focusing on our specific task (MT), we can employ translation-related heuristics to prune the run-time vocabulary precisely and efficiently.


LSH achieves better BLEU than decoding with top frequent words of the same run-time vocabulary size C on attention models.

it in-troduces too large an overhead (50 times slower), especially when softmax is highly optimized on GPU.


The decoder state stores translation information at different granularities, determining which segment should be expressed (phrasal), and which word should be generated (lexical), respectively.

due to the extensive existence of multiword phrases and expressions, the varying speed of the lexical component is much faster than the phrasal one.


Much previous work propose to improve the NMT model by adopting fine-grained translation levels such as the character or sub-word levels, which can learn the intermediate information inside words (Ling et al., 2015;Costa-jussà and Fonollosa, 2016;Chung et al., 2016;Luong et al., 2016;Lee et al., 2016;García-Martínez et al., 2016).

high level structures such as phrases has not been explicitly explored in NMT, which is very useful for machine translation (Koehn et al., 2007).


We find that the chunk boundary could be predicted well, with an average accuracy of 89%, which shows that our model could capture the phrasal boundary information in the translation process.

our model could not predict chunk labels as well as chunk boundaries.


For example, the CRF-BiLSTM POS tagger obtained the state-of-theart performance on Penn Treebank WSJ corpus (Huang et al., 2015).

in low-resource languages, these models are seldom used because of limited labelled data.


In fact, the development of supervised disambiguation systems depends crucially on the availability of re-liable sense-annotated corpora, which are indispensable in order to provide solid training and testing grounds (Pilehvar and Navigli, 2014).

hand-labeled sense annotations are notoriously difficult to obtain on a large scale, and manually curated corpora (Miller et al., 1993;Passonneau et al., 2012) have a limited size.


Nevertheless, the few approaches that have been proposed so far are either focused on treating each individual language in isolation (Otegi et al., 2016), or limited to short and concise definitional text (Camacho-Collados et al., 2016a).

the use of parallel text to perform WSD (Ng et al., 2003;Lefever et al., 2011;Yao et al., 2012;Bonansinga and Bond, 2016) or even Word Sense Induction (Apidianaki, 2013) has been widely explored in the literature, and has demonstrated its effectiveness in producing high-quality sense-annotated data (Chan and Ng, 2005).


Compared to fully characterlevel encoding, the encoder gets word-level embeddings as in the case of unsegmented words (see Figure 1).

the word embedding is intuitively richer than the embedding learned over unsegmented words because of the convolution over characters.


This is expected, since most nouns are outof-vocabulary terms, and therefore get segmented by BPE into smaller, possibly known fragments, which then get confused with other tags.

since the accuracies are quite close, the overall errors are very few and similar between the various systems.


Neural models with minimal feature engineering have achieved competitive performance against traditional methods for the task of Chinese word segmentation.

both training and working procedures of the current neural models are computationally inefficient.


Most existing methods made Markov assumptions to keep the exact search tractable.

2 such assumptions cannot be made in our model as the LSTM component takes advantage of the full segmentation history.


We found 45 cases where INDEP makes an error (and DOMAINENCODING does not) by predicting a wrong comparative or superlative structure (e.g., > instead of ≥).


For instance, Tweet POS tags do not differentiate modal verbs, past tense verbs, and other types of verbs, but categorize all of them as 'V'.

in many forms of counterfactuals, the distinction between modal verbs and past particles from other types of verbs are critical (e.g., in Should / Could / Would Have forms).


(2015), the authors predict user income based on different demographic and psychological features of users.

the process of extracting these features is computationally complex.


We separate this comparison from the rest because of a language-dependent set up.

for Turkish DNN models outperform BiLSTM on seen tokens and yield an almost equal 92.2% accuracy regardless of using the rightmost morphological context.


Replacing the MATE parser with the transition-based YARA does not change the outcome of our monolingual parsing experiment, save for the average 0.58-1.65 drop in UAS.

in cross-lingual parsing, YARA highlights the benefits of not tagging the training data, as the GOLD↝PROJ parsers are there the best choice for parsing 17/26 languages.


In practice we may wish to modify the regression step in an attempt to learn a better transformation matrix A.

the standard first approach of using 2 -regularized (Ridge) regression instead of simple linear regression gives little benefit, even when we have more parameters than word embeddings (i.e.


These experiments consolidate our intuitions from Section 3 that removing common components and frequent words is important and that learning a data-dependent transformation is an effective way to do this.

if we train  Figure 2: Spearman correlation between cosine similarity and human scores for pairs of words in the CRW dataset given an increasing number of contexts per rare word.


We have proposed an unsupervised method which uses co-occurrences statistics to represent the relationship between a given pair of words as a vector.

to neural network models for relation extraction, our model learns relation vectors in an unsupervised way, which means that it can be used for measuring relational similarities and related tasks.


The gap is especially visible for FASTTEXT and SGNS-W2 vectors.

since ATTRACT-REPEL specializes only words seen in linguistic constraints, 5 its performance crucially depends on the coverage of test set words in the constraints.


The two works mentioned above both use a single shared encoder to guarantee the shared latent space.

a concomitant defect is that the shared encoder is weak in keeping the uniqueness of each language.


In Table 4, we can draw the similar conclusion.

different from MultiUN, in the EN-FR-HE group of IWSLT, (X, Z) and (Y, Z) are severely overlapped in Z.


Neural Machine Translation (NMT) models (Bahdanau et al., 2014;Luong et al., 2015;Wu et al., 2016;Vaswani et al., 2017) often operate with fixed word vocabularies, as their training and inference depend heavily on the vocabulary size.

limiting vocabulary size increases the amount of unknown words, which makes the translation inaccurate especially in an open vocabulary setting.


Therefore, we expect to see the same benefit as BPE with the unigram language model.

the unigram language model is more flexible as it is based on a probabilistic language model and can output multiple segmentations with their probabilities, which is an essential requirement for subword regularization.


In order to increase the robustness, they inject noise to input sentences by randomly changing the internal representation of sentences.

these previous approaches often depend on heuristics to generate synthetic noises, which do not always reflect the real noises on training and inference.


On top of the gains with subword regularization, n-best decoding yields further improvements in many language pairs.

we should note that the subword regularization is mandatory for n-best decoding and the BLEU score is degraded in some language pairs without subword regularization (l = 1).


The transformer network is capable of approximating arbitrary squashing functions (Hornik et al., 1989), and can be considered a strong feature extractor with extended receptive fields capable of linking salient features from the entire sequence.

lacking a memory component (as present in the RNN models) prevents the network from modeling a state space, reducing its theoretical strength as a sequence model, thus it requires additional positional information (e.g.


RNMT+ is slower to train than the Transformer Big model on a per-GPU basis.

since the RNMT+ model is quite stable, we were able to offset the lower per-GPU throughput with higher concurrency by increasing the number of model replicas, and hence the overall time to convergence was not slowed down much.


We also observed that additive models are sensitive to the frequency of entities, with high frequency bins having higher conicity than low frequency bins.

no such pattern was observed for multiplicative models and Relation Embeddings: As in entity embeddings, we observe a similar trend when we look at the distribution of ATMs for relation vectors in Figure 3.


From Figure 4 (right), we observe that the average length of entity vectors produced by additive models is also invariant of any changes in number of negative samples.

increase in negative sampling decreases the average entity vector length for all multiplicative models except HolE.


From Figure 5 (left), we observe that the conicity for entity vectors generated by any additive model is almost invariant of increase in dimension, though STransE exhibits a slight decrease.

entity vector from multiplicative models show a clear decreasing pattern with increasing dimension.


The reference summaries get the best score on conciseness since the recent abstractive models tend to copy sentences from the input articles.

our model learns well to select important information and form complete sentences so we even get slightly better scores on informativity and readability than the reference summaries.


Most previous seq2seq models purely depend on the source text to generate summaries.

as reported in many studies (Koehn and Knowles, 2017), the performance of a seq2seq model deteriorates quickly with the increase of the length of generation.


(2017) also proposed to encode human-written sentences to improvement the performance of neural text generation.

they handled the task of Language Modeling and randomly picked an existing sentence in the training corpus.


In the case of SEMoses, meaning preservation is improved when manual UCCA annotation is used.

simplicity degrades, possibly due to the larger number of Scenes marked by the human annotator (TUPA tends to under-predict Scenes).


In the task settings for CrowdFlower, we specified that we needed annotations from six people for each word.

11 because of the way the gold questions work in CrowdFlower, they were annotated by more than six people.


14 Men, women, and other genders are substantially more alike than they are different.

they have encountered different socio-cultural influences for thousands of years.


Similar approaches have been used to 'distantly supervise' annotation of full-text articles describing clinical trials .

to the corpora discussed above, these automatically derived datasets tend to be relatively large, but they include only shallow annotations.


As mentioned, TrueSkill has been used for NLP tasks to infer continuous values for instances.

it is important to note that the support of a Gaussian distribution is unbounded, namely R = (−∞, ∞).


TrueSkill can induce a continuous spectrum of instances (such as skill level of game players) by assuming that each instance is represented as a Gaussian distribution.

the Gaussian distribution has unbounded support, namely R = (−∞, ∞), which does not satisfy the property of absolute bounds for appropriate scalar annotation (i.e., ratio scale in the level of measurement).


Thus, given an annotated score s i which is normalized between [0,1], we change the update functions as follows: This procedure may look similar to DA, where s i is simply accumulated and averaged at the end.


In the standard beam search, the top k words of highest probability are selected deterministically based on the softmax output to expand each hypothesis.

this may lead to suboptimal output for text generation (Wiseman and Rush, 2016), e.g., one beam often dominates and thus inhibits hypothesis diversity.


Table 5 shows that our model with separate decoder and attention over keyphrases produce significantly more informative and relevant arguments than seq2seq trained without evidence.

8 we also observe that human judges prefer the retrieved arguments over generation-based models, illustrating the gap between system arguments and human edited text.


The annotations were published alongside the article.

3 this data only comprises URLs to the original Facebook posts.


As our model is based on populating a latent "event space" into boxes (products of intervals), it is especially reminiscent of the Mondrian process (Roy and Teh, 2009).

the Mondrian process partitions the space as a high dimensional tree (a non-parametric kd-tree), while our model allows the arbitrary box placement required for DAG structure, and is much more tractable in high dimensions compared to the Mondrian's Bayesian non-parametric inference.


They can remember sentence lengths, word identity, and word order (Adi et al., 2017), can capture some syntactic structures such as subject-verb agreement (Linzen et al., 2016), and can model certain kinds of semantic compositionality such as negation and intensification (Li et al., 2016).

all of the previous work studies LSTMs at the sentence level, even though they can potentially encode longer context.


In this paper, we present results only on the dev sets, in order to avoid revealing details about the test sets.

we have confirmed that all results are consistent with those on the test sets.


(2017) have also shown that while LSTMs are aware of which words appear in their context, this awareness degrades with increasing length of the sequence.

the success of copy mechanisms such as attention and caching (Bahdanau et al., 2015;Hill et al., 2016;Merity et al., 2017;Grave et al., 2017a,b) suggests that information in the distant context is very useful.


Thus, the cache is, in a sense, complementary to the standard model, since it especially helps regenerate words from the long-range context where the latter falls short.

the cache also hurts about 36% of the words in PTB and 20% in Wiki, which are words that cannot be copied from context (C none ), as illustrated by bars for "none" in Figure 7.


Some previous work (Srivastava et al., 2017) has explored using language explanations for feature space construction in concept learning tasks, where the problem of learning to interpret language, and learning classifiers is treated jointly.

this approach assumes availability of labeled data for learning classifiers.


While its performance (0.679) suggests that simple binary feedback is a substantial signal, the difference from the full model indicates value in using soft probabilities.

in a sensitivity study, we found the performance of the approach to be robust to small changes in the probability values of quantifiers.


Our approach is surprisingly effective in learning from free-form language.

it does not address linguistic issues such as modifiers (e.g., very likely), nested quantification, etc.


Empirically, S-LSTM can give effective sentence encoding after 3 -6 recurrent steps.

the number of recurrent steps necessary for BiLSTM scales with the size of the sentence.


On the other hand, convolution features embody only fix-sized local ngram information, whereas sentence-level feature aggregation via pooling can lead to loss of information (Sabour et al., 2017).

s-LsTM uses a global sentence-level node to assemble and back-distribute local information in the recurrent state transition process, suffering less information loss compared to pooling.


In light of the benefits of pretraining (Erhan et al., 2010), we should be able to do better than randomly initializing the remaining parameters of our models.

inductive transfer via finetuning has been unsuccessful for NLP (Mou et al., 2016).


Dai and Le (2015) also fine-tune a language model, but overfit with 10k labeled examples and require millions of in-domain documents for good performance.

uLMFiT leverages general-domain pretraining and novel finetuning techniques to prevent overfitting even with only 100 labeled examples and achieves state-ofthe-art results also on small datasets.


The error then increases as the model starts to overfit and knowledge captured through pretraining is lost.

uLMFiT is more stable and suffers from no such catastrophic forgetting; performance remains similar or improves until late epochs, which shows the positive effect of the learning rate schedule.


Later work considered generating queries based on relations extracted by a syntactic parser (Giordani and Moschitti, 2012) and applying techniques from logical parsing research (Poon, 2013).

none of these earlier systems are publicly available, and some required extensive engineering effort for each domain, such as the lexicon used by PRECISE.


We call this a questionbased data split.

many English questions may correspond to the same SQL query.


We favor to the intuitive and efficient way in this work.

the column-cell relation could improve the prediction of the WHERE val-ue.


Characterlevel models (CLM), being a cheaper and accessible alternative to morphology, have been reported as performing competitively on various NLP tasks (Ling et al., 2015;Plank et al., 2016;.

the extent to which these tasks depend on morphology is small; and their relation to semantics is weak.


Our experiments and analysis reveal insights such as: • CLMs provide great improvements over whole-word-level models despite not being able to match the performance of morphology-level models (MLMs) for indomain datasets.

their performance surpass all MLMs on out-of-domain data, • Limitations and strengths differ by morphological typology.


For this reason, we used a model that can be considered small when compared to recent neural SRL models and avoided parameter search.

we wonder how the models behave when given a larger network.


Our results lead to the following conclusions: • For in-domain data, character-level models cannot yet match the performance of morphology-level models.

they still provide considerable advantages over whole-word models, • Their shortcomings depend on the morphology type.


Therefore, given more training data their performance will improve faster than morphology-level models, • They perform substantially well on out of domain data, surpassing all morphology-level models.

relatively less improvement is expected when model complexity is increased, • They generally perform better than models that only have access to predicted/silver morphological tags.


4 Formally, (1) and (2) together with the uniform prior over alignments P (a) form the generative model of AMR graphs.

the alignment model Q ψ (a|c, R, w), as will be explained below, is approximating the intractable posterior P θ,φ (a|c, R, w) within that probabilistic model.


We use this approach also to relax the one-hot encoding of the predicate position (p, see Section 2.4).

the concept prediction model log P θ (c|S t (Φ ψ , Σ), w) relies on the pointing mechanism, i.e.


Some recent work has focused on building aligners specifically for training their parsers (Werling et al., 2015;.

those aligners are trained independently of concept and relation identification and only used at pre-processing.


Such translation models have also been successfully applied to semantic parsing tasks (e.g., (Andreas et al., 2013)), where they rivaled specialized semantic parsers from that period.

they are considerably less accurate than current state-of-the-art parsers applied to the same datasets (e.g., (Dong and Lapata, 2016)).


In particular, van Noord and Bos (2017) used character level seq2seq model and achieved the previous state-of-the-art result.

their model is very data demanding as they needed to train it on additional 100K sentences parsed by other parsers.


an equation system does not encode these information explicitly.


They belong to the same type of problems asking about the summation of consecutive integers.

their meaning representations are very different in the Dolphin language and in equations.


The baseline and shallow models do not perform well on short sentences which despite containing fewer words, can still represent complex meaning which is challenging to capture sequentially.

the performance of the deep model is relatively stable.


Many deep learning architectures have been proposed to model the compositionality in text sequences, requiring a substantial number of parameters and expensive computations.

there has not been a rigorous evaluation regarding the added value of sophisticated compositional functions.


Models with more expressive compositional functions, e.g., RNNs or CNNs, have demonstrated impressive results; however, they are typically computationally expensive, due to the need to estimate hundreds of thousands, if not millions, of parameters (Parikh et al., 2016).

models with simple compositional functions often compute a sentence or document embedding by simply adding, or averaging, over the word embedding of each sequence element obtained via, e.g., word2vec (Mikolov et al., 2013), or GloVe (Pennington et al., 2014).


SWEMs bear close resemblance to Deep Averaging Network (DAN) (Iyyer et al., 2015) or fast-Text (Joulin et al., 2016), where they show that average pooling achieves promising results on certain NLP tasks.

there exist several key differences that make our work unique.


For instance, all words in the fifth column are Chemistry-related.

we do not have a chemistry label in the dataset, and regardless they should belong to the Science topic.


the most vital features for predicting the sentiment of this sentence could be the phrase/sentence 'is just okay', 'not great' or 'makes me wonder why everyone likes', which cannot be captured without MR SST-1 SST-2 Subj TREC RAE (Socher et al., 2011b) 77.7 43.2 82.4 --MV-RNN (Socher et al., 2012) 79.0 44.4 82.9 --LSTM (Tai et al., 2015) -46.4 84.9 --RNN (Zhao et al., 2015) 77.2 --93.7 90.2 Constituency Tree-LSTM (Tai et al., 2015) -51.0 88.0 --Dynamic CNN (Kalchbrenner et al., 2014) -48.5 86.8 -93.0 CNN (Kim, 2014) 81   As demonstrated in Section 4.2.1, word-order information plays a vital role for sentiment analysis tasks.


However, the most vital features for predicting the sentiment of this sentence could be the phrase/sentence 'is just okay', 'not great' or 'makes me wonder why everyone likes', which cannot be captured without MR SST-1 SST-2 Subj TREC RAE (Socher et al., 2011b) 77.7 43.2 82.4 --MV-RNN (Socher et al., 2012) 79.0 44.4 82.9 --LSTM (Tai et al., 2015) -46.4 84.9 --RNN (Zhao et al., 2015) 77.2 --93.7 90.2 Constituency Tree-LSTM (Tai et al., 2015) -51.0 88.0 --Dynamic CNN (Kalchbrenner et al., 2014) -48.5 86.8 -93.0 CNN (Kim, 2014) 81   As demonstrated in Section 4.2.1, word-order information plays a vital role for sentiment analysis tasks.

according to the case study above, the most important features for sentiment prediction may be some key n-gram phrase/words from the input document.


• Sentiment analysis tasks are more sensitive to word-order features than topic categorization tasks.

a simple hierarchical pooling layer proposed here achieves comparable results to LSTM/CNN on sentiment analysis tasks.


PPDB has been used to improve word embeddings (Faruqui et al., 2015;Mrkšić et al., 2016).

ppDB is less useful for learning sentence embeddings .


Also, pragmatic inference is a necessary step toward automatic narrative understanding and generation (Tomai and Forbus, 2010;Ding and Riloff, 2016;Ding et al., 2017).

this type of social commonsense reasoning goes far beyond the widely studied entailment tasks (Bowman et al., 2015;Dagan et al., 2006) and thus falls outside the scope of existing benchmarks.


To improve the performance of Japanese PAS analysis, it is straightforward to increase the size of corpora annotated with PAS.

since it is prohibitively expensive, it is promising to take advantage of a large amount of raw corpora.


ing pre-trained external knowledge in the form of word embeddings has also been ubiquitous.

such external knowledge is overwritten in the task-specific training.


This is especially true for nominals and pronouns, two common types of entity mentions, where the nearest preceding mention that is also compatible in basic properties (e.g., gender, person and number) is likely to co-refer with the current mention.

coreferential event mentions are rarely from the same sentence ( 10%) and are often sentences apart.


Event mentions that occur in the first few paragraphs are more likely to initiate an event chain.

event mentions in later parts of a document may be coreferential with a previously seen event mention but are extremely unlikely to begin a new coreference chain.


It is also important to understand that positionbased features used in entity coreference resolution (Haghighi and Klein, 2007) are usually defined for an entity pair.

we model the distributional patterns of an event chain in a document.


Differently, distant supervision (Mintz et al., 2009;Hoffmann et al., 2011;Surdeanu et al., 2012) is to efficiently generate relational data from plain text for unseen relations with distant supervision (DS).

it naturally brings with some defects: the resulted distantly-supervised training samples are often very noisy (shown in Figure 1), which is the main problem of impeding the performance (Roth et al., 2013).


Most of the current state-of-the-art methods (Zeng et al., 2015;Lin et al., 2016) make the denoising operation in the sentence bag of entity pair, and integrate this process into the distant supervision relation ex-traction.

indeed, these methods can filter a substantial number of noise samples; they overlook the case that all sentences of an entity pair are false positive, which is also the common phenomenon in distant supervision datasets.


manner, where they first conduct entity recognition and then predict relations between extracted entities.

the pipeline framework ignores the relevance of entity identification and relation prediction (Li and Ji, 2014).


The NovelTagging method finds triplets through tagging the words.

they assume that only one tag could be assigned to just one word.


When extracting relation from sentences that contains 1 triplets, NovelTagging model achieve the best performance.

when the number of triplets increases, the performance of NovelTagging model decreases significantly.


Event Type: Transport It is certain that spurious features often result from the semantically pseudo-related context, and during training, a neural network may mistakenly and unconsciously preserve the memory to produce the fakes.

it is difficult to determine which words are pseudo-related in a specific case, and when they will "jump out" to mislead the generation of latent features during testing.


• The RNN models contribute to achieving a higher recall.


Multi-angled cognition enables Hybrid to be more precise.

it is built using a single-channel architecture, concatenating the RNN and the CNN.


At each time step, it chooses what to memorize and forget, so patterns over arbitrary time intervals can be recognized.


For example, sentences are naturally fit units for such models, since a sentence starts only after the preceding sentence is finished, and LSTM may be an adequate tool to process sentences.

when the sequences are not contiguous, as in temporal and other discourse-scale relations, LSTM models do not have the capability to look for input pieces across sequences.


By connecting the output layer, which has a softmax activation, we hand the final decisions made by the pairwise model to GCL.

the hidden layer provides higher layers with cruder but richer information.


By doing the rotation, some pairs in the final chunk of epoch 1 will show up in the first chunk in epoch 2 as well.

within each chunk, we do not randomize pairs, so narrative order is preserved at this level.


Tuning the model for each pair type separately, as well as resampling to deal with class imbalance would, perhaps, improve performance.

the point of these experiments was not to get the largest improvement, but to show that the GCL mechanism can replace heuristic-based timegraph conflict resolution, improving the performance of an otherwise very similar model.


For instance, the Pointer Networks (Vinyals et al., 2015) uses attention over input timesteps.

it has no power to rewrite information for later use, since they have no "memory" except for the RNN states.


All previous works converged to a shared assessment: both CNNs and RNNs provide relevant, but different kinds of information for text classification.

though several works have studied linguistic structures inherent in RNNs, to our knowledge, none of them have focused on CNNs.


As we can see, when the z-test is the highest, the TDS is also the highest and the TDS values are high also for the neighbor words (for example around the word castra).

this is not always the case: for example small words as que or et are also high in z-test but they do not impact the network at the same level.


We also experimented  with Glove (Pennington et al., 2014), which has more vocabulary coverage than word2vec -Glove covers 89.77% of our vocabulary items, whereas word2vec covers 85.66%.

glove did not perform well giving F 1 score of 86% in the standard discrimination task.


To our knowledge, we are the first to use coherence models for this problem.

our goal in this paper is not to build a state-of-the-art system for thread reconstruction, rather to evaluate coherence models by showing its effectiveness in scoring candidate tree hypotheses.


However, our goal in this paper is not to build a state-of-the-art system for thread reconstruction, rather to evaluate coherence models by showing its effectiveness in scoring candidate tree hypotheses.

to previous methods, our approach therefore considers the whole thread structure at once, and computes coherence scores for all possible candidate trees of a conversation.


In English, there are few omissions of arguments, and thus PA is relatively easy, around 83% accuracy , while CR is relatively difficult, around 70% accuracy .

in Japanese and Chinese, where arguments are often omitted, PA is a dif-ficult task, and even state-of-the-art systems only achieve around 50% accuracy.


Although most of studies did not consider the notion entity, Sasano and Kurohashi (2011) consider an entity, and its salience score is calculated based on simple rules.

they used gold coreference links to form the entities, and reported the salience score did not improve the performance.


However, they used gold coreference links to form the entities, and reported the salience score did not improve the performance.

we perform CR automatically, and capture the entity salience by using RNNs.


the performance of ZAR in our baseline model is extremely low, and thus there are few worsen examples and Table 2: Performance of case analysis and zero anaphora resolution for each case, and each argument position for zero anaphora resolution.


The above selectional system restricts the parser's search space sufficiently well that it is feasible to generate an initial MG treebank for many of the sentences in the PTB, particularly the shorter ones and those longer ones which do not require the full range of null heads to be allowed into the chart 16 .

for longer sentences requiring null heads such as extraposers, topicalizers or focalizers, parsing remains impractically slow.


Division by power, i.e., high (SH and LH groups) vs. low (SL and LL groups), does not result in a salient difference in slopes, as it can be seen that the slopes of high (solid) and low (dashed) power lines do not differ much from each other within the same prime utterance length group (indicated by color).

division by prime utterance length, i.e.


We are not denying the existence of accommodation caused by the social distance between interlocutors.

we want to stress the difference between the priming-induced alignment at lower linguistic levels and the intentional accommodation that is caused by higher-level perception of social power.


We manually inspected 30 reference answers which were annotated incorrectly and found that of those, about 95% were indeed incorrect.

62% are actually answerable from some paragraph, indicating that the real ceiling performance on this dataset is around 90% and that there is still room for improvement on this task.


Sentence scoring and sentence selection are two main steps in extractive document summarization systems.

previous works treat them as two separated subtasks.


On one hand, the RNN is used to remember the partial output summary by feeding the selected sentence into it.

it is used to provide a sentence extraction state that can be used to score sentences with their representations.


One is used to rank the sentences to select the first sentence, and the other one is used to model the redundancy during sentence selection.

their model of measuring the redundancy only considers the redundancy between the sentence that has the maximal score, which lacks the modeling of all the selection history.


We think the second step selection benefits from the first step in NEUSUM since it can remember the selection history, while the separated models lack this ability.

we can notice the trend that the precision drops fast after each selection.


Paths having less than z words or that do not contain a verb are filtered out (z is a tuning parameter).

unlike in Boudin and Morin (2013), we rerank the K best paths with the following novel weighting scheme (the lower, the better), and the path with the lowest score is used as the compression: The denominator takes into account the length of the path, and its fluency (F ), coverage (C), and diversity (D).


This set of sentences can already be considered to be a summary of the meeting.

it might exceed the maximum size allowed, and still contain some redundancy or off-topic sections unrelated to the general theme of the meeting (e.g., chit-chat).